---
title: "CohereChatGenerator"
id: coherechatgenerator
slug: "/coherechatgenerator"
description: "CohereChatGenerator enables chat completions using Cohere's large language models (LLMs)."
---

# CohereChatGenerator

CohereChatGenerator enables chat completions using Cohere's large language models (LLMs).

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx) |
| **Mandatory init variables** | `api_key`: The Cohere API key. Can be set with `COHERE_API_KEY` or `CO_API_KEY` env var. |
| **Mandatory run variables** | `messages` A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects |
| **Output variables** | `replies`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Cohere](/reference/integrations-cohere) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere |

</div>

This integration supports Cohere `chat` models such as `command`,`command-r` and `comman-r-plus`. Check out the most recent full list in [Cohere documentation](https://docs.cohere.com/reference/chat).

## Overview

`CohereChatGenerator` needs a Cohere API key to work. You can set this key in:

- The `api_key` init parameter using [Secret API](../../concepts/secret-management.mdx)
- The `COHERE_API_KEY` environment variable (recommended)

Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the `Co.chat` method directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the Cohere API, refer to the [Cohere documentation](https://docs.cohere.com/reference/chat).

Finally, the component needs a list of `ChatMessage` objects to operate. `ChatMessage` is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata.

### Tool Support

`CohereChatGenerator` supports function calling through the `tools` parameter, which accepts flexible tool configurations:

- **A list of Tool objects**: Pass individual tools as a list
- **A single Toolset**: Pass an entire Toolset directly
- **Mixed Tools and Toolsets**: Combine multiple Toolsets with standalone tools in a single list

This allows you to organize related tools into logical groups while also including standalone tools as needed.

```python
from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.cohere import CohereChatGenerator

# Create individual tools
weather_tool = Tool(name="weather", description="Get weather info", ...)
news_tool = Tool(name="news", description="Get latest news", ...)

# Group related tools into a toolset
math_toolset = Toolset([add_tool, subtract_tool, multiply_tool])

# Pass mixed tools and toolsets to the generator
generator = CohereChatGenerator(
    tools=[math_toolset, weather_tool, news_tool]  # Mix of Toolset and Tool objects
)
```

For more details on working with tools, see the [Tool](../../tools/tool.mdx) and [Toolset](../../tools/toolset.mdx) documentation.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

You need to install `cohere-haystack` package to use the  `CohereChatGenerator`:

```shell
pip install cohere-haystack
```

#### On its own

```python
from haystack_integrations.components.generators.cohere import CohereChatGenerator
from haystack.dataclasses import ChatMessage

generator = CohereChatGenerator()
message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
print(generator.run([message]))
```

With multimodal inputs:

```python
from haystack.dataclasses import ChatMessage, ImageContent
from haystack_integrations.components.generators.cohere import CohereChatGenerator

# Use a multimodal model like Command A Vision
llm = CohereChatGenerator(model="command-a-vision-07-2025")

image = ImageContent.from_file_path("apple.jpg")
user_message = ChatMessage.from_user(content_parts=[
	"What does the image show? Max 5 words.",
	image
	])

response = llm.run([user_message])["replies"][0].text
print(response)

# Red apple on straw.
```

#### In a Pipeline

You can also use `CohereChatGenerator` to use cohere chat models in your pipeline.

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.cohere import CohereChatGenerator
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", CohereChatGenerator())
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system("You are an assistant giving out valuable information to language learners.")
messages = [system_message, ChatMessage.from_user("What's the official language of {{ country }}?")]

res = pipe.run(data={"prompt_builder": {"template_variables": {"country": country}, "template": messages}})
print(res)

```
